Testing two y axis with ggplot
rrwiren
2018-04-04
Trying ggplot, since had some POSIXct x axis problems with RMarkdown
(Edit: got it finally working also with simple plot: http://rpubs.com/rrwiren/377796)
library(ggplot2)We start with loading some data and attach the data frame for easier access
e20180301 <- read.csv("~/Downloads/energydatamod/2018-03-01_twmod.csv", sep=";")
attach(e20180301)Always good to explore the data, specially the date and time formats
head(e20180301)## Start End Consumption Status SPOT Temperature
## 1 2018-03-01 00:00 2018-03-01 01:00 3.44 Ok 4.68224 -16.4
## 2 2018-03-01 01:00 2018-03-01 02:00 4.38 Ok 4.90048 -16.5
## 3 2018-03-01 02:00 2018-03-01 03:00 4.18 Ok 4.84220 -16.9
## 4 2018-03-01 03:00 2018-03-01 04:00 3.79 Ok 4.81492 -16.7
## 5 2018-03-01 04:00 2018-03-01 05:00 4.37 Ok 4.83848 -17.1
## 6 2018-03-01 05:00 2018-03-01 06:00 3.52 Ok 4.91040 -17.7str(Start)## Factor w/ 24 levels "2018-03-01 00:00",..: 1 2 3 4 5 6 7 8 9 10 ...unclass(Start)## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [24] 24
## attr(,"levels")
## [1] "2018-03-01 00:00" "2018-03-01 01:00" "2018-03-01 02:00"
## [4] "2018-03-01 03:00" "2018-03-01 04:00" "2018-03-01 05:00"
## [7] "2018-03-01 06:00" "2018-03-01 07:00" "2018-03-01 08:00"
## [10] "2018-03-01 09:00" "2018-03-01 10:00" "2018-03-01 11:00"
## [13] "2018-03-01 12:00" "2018-03-01 13:00" "2018-03-01 14:00"
## [16] "2018-03-01 15:00" "2018-03-01 16:00" "2018-03-01 17:00"
## [19] "2018-03-01 18:00" "2018-03-01 19:00" "2018-03-01 20:00"
## [22] "2018-03-01 21:00" "2018-03-01 22:00" "2018-03-01 23:00"We change this here because dates and times are actually stored as factors
dateTime <- as.POSIXlt(Start)
str(dateTime)## POSIXlt[1:24], format: "2018-03-01 00:00:00" "2018-03-01 01:00:00" ...unclass(dateTime)## $sec
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## $min
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## $hour
## [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
## [24] 23
##
## $mday
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##
## $mon
## [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
##
## $year
## [1] 118 118 118 118 118 118 118 118 118 118 118 118 118 118 118 118 118
## [18] 118 118 118 118 118 118 118
##
## $wday
## [1] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
##
## $yday
## [1] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
## [24] 59
##
## $isdst
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## $zone
## [1] "EET" "EET" "EET" "EET" "EET" "EET" "EET" "EET" "EET" "EET" "EET"
## [12] "EET" "EET" "EET" "EET" "EET" "EET" "EET" "EET" "EET" "EET" "EET"
## [23] "EET" "EET"
##
## $gmtoff
## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [24] NAWe start with plotting the hourly Consumption data
p <- ggplot(e20180301, aes(x = dateTime))
p <- p + geom_line(aes(y = Consumption))Then we add Temperature
p <- p + geom_line(aes(y = Temperature))
p
Next we continue and create the plot with two data variables
Again we start by plotting the Consumption
p <- ggplot(e20180301, aes(x = dateTime))
p <- p + geom_line(aes(y = Consumption, colour = "Consumption"))Here we add the Temperature, transformed to match roughly the range of the Consumption
p <- p + geom_line(aes(y = Temperature/3, colour = "Temperature"))Now adding the secondary axis, following the example in the help file ?scale_y_continuous
and, very important, reverting the above transformation
p <- p + scale_y_continuous(sec.axis = sec_axis(~.*3, name = "Temepertaure (°C)"))Modifying colours and theme options
p <- p + scale_colour_manual(values = c("blue", "red"))
p <- p + labs(y = "Consumption (kWh)",
x = "Date and time",
colour = "Parameter")
p <- p + theme(legend.position = c(0.8, 0.9))
p